
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "56"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 77 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 73 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	27.57%	0.519918	-0.655613	0.533709	0.700942	-0.480421	2.043359	-0.152767	0.319137	0.7795	0.7723	0.2287	5.778504	5.428725
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	11.83%	-0.602293	0.993866	1.117561	1.467656	-0.034987	2.308423	-0.437179	2.196306	0.7077	0.6939	0.2166	2.314819	2.185949
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.126679	0.021317	-0.119416	-0.034944	0.637851	1.257905	1.492274	0.073353	0.0520	0.0446	0.0008	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.621972	0.952564	1.350793	2.280925	-0.628132	2.557635	0.625261	6.122019	0.6113	0.6120	0.3198	6.710805	6.134272
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.377835	1.216001	0.683831	1.300772	0.140444	5.581816	0.222707	5.719137	0.6115	0.6093	0.3143	5.813613	5.136951
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	35.48%	-1.054850	1.165897	0.059459	0.074866	-0.852351	0.648701	-0.624679	0.143306	0.6834	0.6678	0.2408	2.963202	2.589889
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	2.69%	-0.265552	0.633971	1.515072	1.600675	-0.865092	2.636684	-0.632366	2.212752	0.7614	0.7416	0.1951	4.285036	3.320787
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.220426	2.134764	8.760639	10.122720	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	6.955812	6.018787
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	2.16%	28.65%	-0.473549	0.097588	0.445514	0.792937	0.142719	2.050232	-0.612665	2.190887	0.6196	0.6197	0.3037	2.009399	1.895141
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.557455	2.101878	0.637788	0.917850	0.003392	-0.149006	0.363489	0.012953	0.0459	0.0386	0.0006	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.793216	5.212639	9.157370	6.643156	12.894364	23.312767	38.340952	38.499307	0.5776	0.5788	0.3124	10.448839	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.713861	0.346589	3.132386	3.666543	1.571621	5.334683	-0.935321	-0.074224	0.6227	0.6194	0.3260	5.958723	4.953380
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.441358	0.883520	3.042280	2.976359	2.287666	6.299499	-1.075550	-0.412364	0.6045	0.6017	0.3340	5.096624	3.761821
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.572799	0.552144	2.560611	3.347292	1.732948	5.266905	-0.803137	-0.122395	0.6215	0.6191	0.3259	4.067184	3.579107
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.573160	0.669095	3.475046	3.560966	2.595936	6.160685	-0.424777	3.183537	0.6199	0.6233	0.3261	7.913059	5.969030
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.385160	0.903664	4.100284	4.034927	2.367148	6.094885	-1.025354	1.190005	0.6151	0.6185	0.3276	5.357556	4.415790
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.981922	1.733606	0.428343	1.073934	-0.335631	0.991429	-0.247429	-0.665770	0.0470	0.0392	0.0016	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.554592	0.469591	2.445020	2.959992	1.519363	5.034778	-0.741727	1.677551	0.6220	0.6237	0.3229	4.940126	3.982821
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	1.08%	-0.475445	0.020298	1.682772	1.764548	1.336615	2.894572	-1.054683	-0.888420	0.6290	0.6335	0.3267	1.533755	1.519934
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	9.19%	2.16%	-0.755668	0.940300	1.559699	1.678672	0.542208	2.275272	-0.785426	1.545483	0.6352	0.6384	0.3258	1.622386	1.510651
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.392886	2.522527	4.208720	4.856922	1.953106	7.155092	-0.102657	7.515283	0.6279	0.6236	0.3278	0.000000	0.000000
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.160251	2.493503	1.779406	1.898075	1.002686	3.555433	-0.386140	5.174797	0.6253	0.6225	0.3277	5.634291	4.796355
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.970760	1.455601	0.448117	0.881245	-0.539704	1.322016	-0.407379	-0.225034	0.0407	0.0411	0.0012	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	0.371417	0.378130	0.214532	0.324690	-0.028644	0.407055	-0.484897	-0.022734	0.6149	0.6162	0.3091	1.365871	1.320279
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	0.00%	-0.624656	1.293119	1.915757	2.243508	1.080874	1.356200	1.098217	2.008220	0.7357	0.7327	0.2028	1.909038	1.736683
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.327761	1.641686	2.624302	3.007506	1.504551	4.862859	-0.457361	3.684776	0.6160	0.6196	0.3415	2.853173	2.547866
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	101145.661413	126326.874038	4974095.770800	4988647.454360	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	4.05%	2.464953	2.529044	2.903660	2.841897	0.216955	0.144197	-1.776553	-0.559654	0.6316	0.6215	0.3248	1.950973	1.640423
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.450163	6.442283	24.063451	24.242722	13.081125	16.402177	-1.435097	-0.411220	0.6112	0.6048	0.3331	5.257109	4.719995
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	1.62%	1.446674	1.424022	1.015320	1.007338	-0.074996	0.116328	-1.328722	-0.544838	0.6207	0.6179	0.3245	1.742907	1.673271
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	80.00%	2.077630	2.031508	1.584806	1.650896	0.348587	0.628793	-1.879060	-1.410838	0.6255	0.6255	0.3232	4.950923	3.979287
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	75.14%	1.598893	1.701652	1.178354	1.410447	0.355794	1.000025	-1.527538	-0.748587	0.6039	0.5999	0.3355	3.295523	2.860662
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	83.87%	2.167892	2.347122	1.732134	1.856329	0.748640	1.420603	-1.405381	-0.678648	0.6291	0.6257	0.3223	3.421734	2.699783
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0497	0.0440	0.0012	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.787475	7.200186	13.439053	13.091779	12.932120	14.715036	-1.952685	-0.368872	0.6359	0.6229	0.3217	6.918020	4.849836
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6147	0.6022	0.3336	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6286	0.6162	0.3304	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.565817	7.668829	17.417433	17.947460	19.450135	22.265908	0.301751	2.621171	0.6413	0.6292	0.3290	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	72.04%	1.563298	1.849134	1.510220	1.660766	0.198311	1.132390	-1.988449	-0.749977	0.6279	0.6196	0.3221	3.844431	2.863775
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0437	0.0365	0.0020	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.946389	6.301366	12.920064	13.221998	13.875894	15.988991	-2.123480	-0.272305	0.6316	0.6225	0.3314	5.295611	4.511521
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.651196	6.660328	25.728561	26.436767	0.443994	1.102999	-1.978626	1.148341	0.6912	0.6790	0.3061	5.244264	3.668646
2459541	digital_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	6.070989	5.678920	15.998377	15.525184	10.079441	12.158634	4.166427	5.266539	0.6905	0.6735	0.2819	2.567920	2.274878
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	58.48%	3.228782	3.170347	3.031517	2.905563	0.995335	1.684114	-0.932713	0.364893	0.6925	0.6743	0.3023	3.316318	2.566035
2459536	digital_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	8.942000	9.590983	21.212976	21.566303	20.162004	21.820278	-3.483466	-1.036237	0.4253	0.4238	0.2074	1.604758	1.388580
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.245546	2.333274	2.414075	2.572815	2.498965	1.520463	-0.601468	-0.329197	0.0737	0.0698	0.0105	1.208915	1.208290
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.781800	2.102825	1.567148	1.764199	-0.307319	0.493932	-1.245203	-0.152819	0.0900	0.0820	0.0111	1.171957	1.167596
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.077486	7.139714	13.599700	13.690179	11.960042	14.888935	-2.513334	-0.810689	0.0812	0.0776	0.0105	1.198473	1.200364
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.289695	8.319445	17.733643	17.404728	14.766171	17.626252	-2.164672	-1.427013	0.0736	0.0754	0.0103	0.837142	0.835517
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.698810	1.936317	1.377167	1.434573	0.320384	1.225791	-1.469955	0.059993	0.0808	0.0822	0.0123	1.185123	1.182900
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.729263	8.905247	14.461365	14.457115	28.623999	24.676907	-1.774424	-0.199581	0.9054	0.9322	0.2913	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	7.655852	7.423464	19.570987	20.362602	11.498782	20.244628	-5.516411	-5.274315	nan	nan	nan	13.958275	11.034817
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.454804	7.338063	17.822791	17.158732	7.379750	16.249193	-2.962658	-1.169475	0.7060	0.6575	0.3230	2.384207	2.206146
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	5.498571	5.436054	9.578263	9.866406	2.510841	3.083517	-1.058304	-1.184054	0.0564	0.0420	0.0028	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.556750	9.786360	11.673055	11.213488	12.125491	10.392030	10.396017	12.468279	0.0766	0.0897	0.0066	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	79.952961	90.803568	99.208754	119.674854	123.456368	129.027552	594.164048	724.654891	0.0114	0.0083	0.0058	1.093710	1.084568

Antenna Season Report Notebook¶

Antenna 56 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 56, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 56: 2459597

Antenna 56: 2459596

Antenna 56: 2459595

Antenna 56: 2459594

Antenna 56: 2459593

Antenna 56: 2459592

Antenna 56: 2459591

Antenna 56: 2459590

Antenna 56: 2459589

Antenna 56: 2459588

Antenna 56: 2459587

Antenna 56: 2459586

Antenna 56: 2459585

Antenna 56: 2459584

Antenna 56: 2459583

Antenna 56: 2459582

Antenna 56: 2459581

Antenna 56: 2459580

Antenna 56: 2459579

Antenna 56: 2459578

Antenna 56: 2459577

Antenna 56: 2459576

Antenna 56: 2459575

Antenna 56: 2459574

Antenna 56: 2459573

Antenna 56: 2459572

Antenna 56: 2459571

Antenna 56: 2459570

Antenna 56: 2459569

Antenna 56: 2459566

Antenna 56: 2459565

Antenna 56: 2459564

Antenna 56: 2459563

Antenna 56: 2459562

Antenna 56: 2459561

Antenna 56: 2459560

Antenna 56: 2459559

Antenna 56: 2459558

Antenna 56: 2459556

Antenna 56: 2459552

Antenna 56: 2459551

Antenna 56: 2459549

Antenna 56: 2459542

Antenna 56: 2459541

Antenna 56: 2459540

Antenna 56: 2459536

Antenna 56: 2459535

Antenna 56: 2459534

Antenna 56: 2459533

Antenna 56: 2459532

Antenna 56: 2459530

Antenna 56: 2459527

Antenna 56: 2459524

Antenna 56: 2459523

Antenna 56: 2459522

Antenna 56: 2459513

Antenna 56: 2459508

Antenna 56: 2459506

Antenna 56: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶